Podcast
Questions and Answers
Match the components of decision analysis with their descriptions:
Match the components of decision analysis with their descriptions:
Decision Alternatives = Different courses of action available to a decision maker States of Nature = Uncertain future events that affect the outcome Payoffs = Consequences associated with decision alternatives Problem Formulation = The initial step in decision analysis outlining the problem
Match the terms used in decision analysis with their definitions:
Match the terms used in decision analysis with their definitions:
Decision Alternatives = Options among which choices are made States of Nature = Possible scenarios that can occur Payoffs = Results or outcomes from selected options Combinations = Pairs formed from decision alternatives and states of nature
Match the elements of a decision problem with their roles:
Match the elements of a decision problem with their roles:
Decision Alternatives = Represent strategies or choices States of Nature = Serve as variables in uncertain environments Payoffs = Indicate the results of decisions Problem Formulation = Establishes the framework of the decision issue
Match the types of analysis used in problem formulation with their purposes:
Match the types of analysis used in problem formulation with their purposes:
Match the chapters and concepts in 'Statistics for Business and Economics' with their focus areas:
Match the chapters and concepts in 'Statistics for Business and Economics' with their focus areas:
Match the following terms related to decision making with their definitions:
Match the following terms related to decision making with their definitions:
Match the following concepts with their descriptions in decision making:
Match the following concepts with their descriptions in decision making:
Match the following aspects of decision making with their characteristics:
Match the following aspects of decision making with their characteristics:
Match the following decision-making terms with their examples:
Match the following decision-making terms with their examples:
Match the following strategies with their related concepts:
Match the following strategies with their related concepts:
Flashcards
Decision Analysis
Decision Analysis
A process for making decisions under uncertainty, involving identifying decision alternatives, states of nature, and potential payoffs.
Problem Formulation
Problem Formulation
The first step in decision analysis, where the problem is described, and decision alternatives, states of uncertainty, and consequences are identified.
Decision Alternatives
Decision Alternatives
Possible courses of action in a decision-making situation.
State of Nature
State of Nature
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Payoffs
Payoffs
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Mutually Exclusive States of Nature
Mutually Exclusive States of Nature
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Collectively Exhaustive States of Nature
Collectively Exhaustive States of Nature
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Study Notes
Statistics for Business and Economics (13e)
- Authored by Anderson, Sweeney, Williams, Camm, Cochran
- Published by Cengage Learning in 2017
- Slides by John Loucks, St. Edwards University
- Includes modifications by Reid Kerr
Chapter 21: Decision Analysis
- Topics include Problem Formulation, Decision Making with Probabilities, Decision Analysis with Sample Information, and Computing Branch Probabilities using Bayes' Theorem
Problem Formulation
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The first step in decision analysis involves defining the problem verbally.
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Decision alternatives, states of nature (uncertain future events), and payoffs (outcomes) must be identified.
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A specific combination leads to a payoff.
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Payoffs can be in terms of profit, cost, time, distance, or any other measurement.
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States of nature should be mutually exclusive and collectively exhaustive.
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A decision problem is characterized by decision alternatives, states of nature, and resulting payoffs.
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The decision alternatives are the possible strategies a decision-maker may employ.
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States of nature refer to future events beyond the control of the decision-maker.
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States of nature must be mutually exclusive and collectively exhaustive.
Payoff Tables
- The consequence, resulting from a specific combination of decision alternative and state of nature, is a payoff.
- A payoff table shows all possible combinations of decision alternatives and states of nature with associated payoffs.
- Payoffs can be expressed in various ways, including profit, cost, time, distance, or other relevant measurements.
Table 21.1
- Payoff table for PDC condominium project
- Payoffs are in millions of dollars
- Shows payoffs for three possible scenarios (30, 60, 90 condominiums) and two states of nature: strong demand and weak demand
Decision Trees
- A graphical representation showing the sequential nature of the decision-making process.
- Two types of nodes:
- Round nodes represent states of nature
- Square nodes represent decision alternatives.
- Branches from round nodes indicate the various states of nature.
- Branches from square nodes represent various decision alternatives.
- Payoffs are at the end of each branch.
Decision Making with Probabilities
- After defining decision alternatives and states of nature, probabilities must be determined, often using the frequency method or a subjective method.
- Probabilities must satisfy two conditions:
- P(s) ≥ 0 for all states of nature
- Σ P(s) = 1, where N represents all possible states of nature
Decision Making Under "Uncertainty"
- Maximin: Choose alternative that maximizes the minimum possible payoff (pessimistic).
- Maximax: Choose alternative that maximizes the maximum possible payoff (optimistic).
- Expected Monetary Value (EMV): Using prior probabilities for states of nature, calculate the expected payoff for each alternative. Select the alternative with the largest EMV.
Expected Value Approach
- The expected value of a decision alternative is the sum of the weighted payoffs.
- The expected value (EV) of decision alternative d is defined as EV(d) = Σ P(s)V(d, s) , where N is the number of states of nature, P(s) is the probability of state s, and V(d, s) is the payoff corresponding to decision alternative d and state s.
Expected Value of Perfect Information (EVPI)
- The increase in expected profit if the state of nature is known with certainty.
- Provides an upper bound on the expected value of any sample or survey information.
Computing Branch Probabilities Using Bayes' Theorem
- Bayes' Theorem can be used to determine branch probabilities in decision trees.
- Prior probabilities are initial probabilities for states of nature.
- Conditional probabilities are probabilities of outcomes given a state of nature.
Expected Value of Sample Information
- The additional expected profit possible from knowledge of the sample or survey information (EVSI).
- Calculated by subtracting the expected value without sample information (EVwoSI) from the expected value with sample information (EVwSI).
- Determines the value of acquiring more information.
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